Data science jobs are an essential part of the growing tech sector. This guide contains an overview of data science jobs, including salary info and career tracks.
It’s no secret that data is powering the next wave of global innovation. This is most frequently discussed around machine learning and artificial intelligence. Still, other key skill sets like data refining, visualization, database management, and even basic data analysis skills contribute to a new frontier of technology and business. This has rocked the job market’s demand for both programming and statistics aptitude, giving way to the career path that combines the two: the data scientist. A relatively unknown field before the 2010s, data science has become extremely popular among both employers and prospective employees. As the amount of data created continues to climb exponentially, so will the demand for data experts into the 2020s and beyond.
But what does the barrier to entry look like to break into this field? Do you need an advanced degree? While many once contended that a Ph.D. was a must for true data scientists, the sheer demand (in addition to cultural shifts in the tech scene) has led to much more leniency on required degrees, allowing those with “just” a master’s and bachelor’s degree to easily break into the scene (Anecdotally, I am a senior data scientist and my highest level of education is a bachelor’s degree).
Some major tech firms are eliminating degree requirements—if you have the skills, you can have the job. Because of this cultural shift, there has been a surge in data science bootcamps, aimed as an alternative to advanced degrees, that are quicker, cheaper, and more focused on career placement than traditional academic programs. For an even more affordable and independent route, the internet has become highly saturated with inexpensive/free data science content via Youtube, Coursera, Udemy, and other video aggregators. Any of these routes to gaining data science skills can assist you in acquiring the required skills to be a data professional.
That isn’t to say that experience and degrees don’t play a substantial role in your job search. Not only does experience make finding a job substantially easier, but it can play a considerable role in dictating your salary.
Data Scientist Salary
Like most positions, data science salaries fluctuate based on two primary factors: geographic region and previous work experience. As discussed below, payment by region has been somewhat clouded by the emergence of remote positions. However, you’ll still tend to see jobs based out of New York or San Francisco paying far above the national average. While the national average salary for a data scientist hovers around $100,000/year, California-based positions often pay more on average in the $125,000-$130,000/year range.
With that said, there’s no bigger factor in determining salary than experience, which includes both hands-on professional experience and academic experience. According to the University of Wisconsin Research, these positions can generally be bucketed into various categories, including entry-level, which sees a median salary of $95,000. Next, mid-level data scientists (those with a few years of experience) see median salaries around $130,000 and upwards of $195,000 if the position is managerial. For data scientists firmly classified as “experienced,” the median salary rises to $165,000/year and upwards of $250,000/year for managerial roles.
This emphasis on experience shapes the data science professional market quite a bit. Because of the demand for experienced data scientists, many of these positions see a very high turnover rate compared to other positions, as it’s easy to climb up the fiscal ladder market-wide as you accumulate years of experience. Many employers don’t raise their data scientists’ salaries with their increasing market value. Thus it’s not uncommon to see data scientists moving between companies every 12 to 18 months, especially in popular urban areas. While this does provide a challenge for employers, seasoned data scientists are afforded a high amount of leverage in dictating pay on their terms.
I’d also like to highlight the market rate for freelance data scientists, as many choose to go the fully self-employed route on Upwork, Fiverr, or another freelance-focused site. For an “entry-level” American data scientist, $30-$40/hour is not an uncommon rate. With experience and a portfolio of projects and skills, it’s normal to see data scientists begin to charge upwards of $50, $75, or even $100/hour and beyond. While sustaining a high volume of hours on freelance work isn’t as easy as signing on to a 40-hour/week full-time position, it offers uncapped, potentially lucrative earning potential that may outmatch a standard position, even with less than 40 hours/week of work.
Data Scientist Positions
It’s widely known that data science as an umbrella term can be extremely vague. One company’s data scientist can be another company’s data engineer, which could have the same requirements as another company’s machine learning engineer. Navigating these position names can be difficult, but as a very general rule of thumb, the umbrella of entry-level positions that stem from “Data Scientist” runs as follows:
Entry-level Data Science Jobs
Business Analyst: Focuses more on business-specific metrics and KPIs. Typically not as development-heavy, but often uses business intelligence software like Tableau and PowerBI to analyze data and build dashboards. According to salary.com, the average starting salary for a business analyst is about $70,000, according to salary.com.
Data Analyst: Especially broad, but in a data science context, you’ll often see the data analyst position focusing on SQL and data extraction, in addition to many of the more business-oriented criteria described with a business analyst. The average starting salary for an entry-level data analyst is $41,374, according to Glassdoor.
Data Visualization Analyst: This is a slightly rarer position, but you will find positions that make heavy use of Python (Seaborn, Matplotlib) and R (ggplot2) custom visualization tools as a data visualization analyst. A common way to think of this position is by looking at FiveThirtyEight, which leverages visualization specialists to create visualizations that are unique and specific to the content being discussed in each article. This position is one part developer, one part graphic designer, one part statistician, and one part storyteller.
Data Scientist: Contrary to popular belief, there are entry-level Data Scientist positions, whether the “Junior Data Scientist” or the more standard “Data Scientist.” Any of the above positions could be labeled as a data scientist, so it’s very important to consider how these all bleed into one another. However, a data scientist is someone with Python or R proficiency (in addition to SQL) who knows how to analyze, visualize, and most importantly, strategize how to create value from data. A little more code-intensive than a data analyst, a little more statistically-oriented than a Data Engineer. Right in the middle of the statistics-to-computer-science spectrum. According to Glassdoor, the average entry-level data scientist salary is $93,167.
Senior Data Science Jobs
If you’re just breaking into the field, these positions generally encompass the type of entry-level positions you may see. As you progress further into your career, some titles that may be more geared to those with years of experience include:
Senior Data Scientist: Same criteria as the “Data Scientist” described above. However, the Senior Data Scientist may emphasize managing a team or tackling more complicated problems in general.
Machine Learning Engineer: Hyper-focused on training and optimizing machine learning algorithms, with an occasional emphasis on model deployment and recursive training of algorithms from user feedback. This is typically reserved for someone with several years of experience or a math/computer science-heavy advanced degree. The average salary for a senior machine learning engineer is $158,508, according to Glassdoor.
Data Engineer: Drives efforts on model deployment and data transfer. Something as simple as “getting data from Point A to Point B” seems simple but can involve a massive amount of technical infrastructure, which is usually where the data engineer comes into play. If data science positions exist on a statistics-to-computer-science spectrum, the data engineer is much closer to the computer science side. The average salary for a senior data engineer is $135,961, according to Glassdoor.
Database Administrator/Data Architect: This would be even further toward the computer science side of the spectrum. Setting up internal infrastructure around how data is captured and stored can be a lengthy and highly-technical process. Products like AWS, Snowflake, and Google Cloud are becoming increasingly accessible to make this a more painless process. However, there is still a substantial need for data architects and database specialists to help guide this process.
It’s important to note that many experienced data science professionals with 10+ years of experience can still carry the title of “Data Analyst,” “Business Analyst,” etc. There’s no written-in-stone hierarchy or seniority to the positions.
Where to Find Data Scientist Jobs
Where you choose to find jobs depends slightly on what kind of job you’re seeking. As far as internet job boards go, it probably won’t surprise you that I’ve consistently found the highest volume of quality jobs on LinkedIn. I’ve found listings on Linkedin to be the most transparent, and they feature resources to contact a recruiter associated with the company you’re applying to. LinkedIn also offers robust filtering tools if you’d like to get specific with your search. Modern companies are generally very focused on their LinkedIn presence, so I think this is a great place to get started.
For a similar flavor of job prospects, Indeed.com also serves as a solid choice for a similar flavor of job prospects with a high volume of listings. Both LinkedIn and Indeed are great for those seeking mid-sized to large companies and companies that are generally well-funded and focused on bringing in employees for salary-based competition.
While the above will cover what most employees are seeking, there are various sites dedicated to serving those who are more interested in the early-stage startup market. For this, I highly recommend creating an account on AngelList, which focuses on forming connections and hires within the startup market. Jobs on Angellist are often listed with their associated equity packages, and some of the earlier stage companies may be offering a fairly large piece of their company. Still, most (but not all) jobs you’ll see on Angellist also come with a normal salary, benefits, time off, etc. You can also leverage the filters on the site to fit where you are on the salary/equity spectrum, among other criteria.
Many have found success searching for jobs through coding platforms like HackerRank/Leetcode, where prospective employers can more easily assess you based on the difficulty of the questions you complete on the website. This isn’t an ideal place to start for a beginner, but if you’re a skilled developer (particularly in Python, for data scientists) and want to show it off, this is a creative route to go.
Finally, I’d be remiss if I didn’t discuss the old-fashioned way of finding jobs: making in-person connections. Leveraging your current professional network, expanding your network via networking events (some of these do happen online too!), and attending hiring events is a fantastic and likely more effective way to find interested employers. Of course, the internet provides you an extremely easy way to get dozens, even hundreds of applications out in a short time, while in-person interactions take a lot more time and energy. Still, if you want to prioritize quality job outreach over a ton of shots on goal, definitely see how you can connect with prospective employers in person or via networking events.
Remote Data Science Jobs
With the rise of the “digital nomad” and remote work as a whole over the 2010s, developers became the poster boy for the job you can do from anywhere. Data science followed suit, with a healthy amount of positions opening up that allowed work-from-home flexibility. Still, remote work was typically a niche perk that you would find if you hyper-targeted your job search towards it, using websites and job aggregators that focused on remote working.
That all changed in 2020 with the COVID-19 crisis. Remote work became not only the norm throughout the pandemic, but companies throughout tech and beyond began enforcing flexible work policies that they intend to keep in place after the pandemic ends. Tobi Lutke of Shopify, Jack Dorsey of Square and Twitter, and many other tech leaders have announced that their companies will enforce a remote-work-by-default policy, allowing them to expand their eligible talent pool substantially and reduce some overhead costs. While no one can predict if remote work norms will ever revert to what they were pre-2020, it is generally believed that remote positions for developers and data scientists will remain not just widely available but expected.
Still, if remote work is something you’d like to avoid, there are certainly options. Many remote positions are “flexible” and offer an in-office alternative. Additionally, more traditional companies, particularly in finance, may default to remaining in-person, even for data scientists. Luckily, most job sites have introduced remote work filters over the last 18 months, as they know the labor market has gained a lot of leverage in demanding that positions be remote.
The data science job market remains in a place that is exciting, growing, and lucrative but also challenging to understand and digest. Vague job descriptions, fluid salary expectations, and a skillset that definitely cannot be learned overnight all contribute to some of the barriers to approaching and entering this market. However, with appropriate studying and research, anyone can set out on this path, accumulating experience, and building their resume and brand that employers and recruiters will undoubtedly be chasing.